Abstract
Dl 4 Tsc is an open-source AI & Machine Learning project. Deep Learning for Time Series Classification. This is the companion repository for our paper titled "Deep learning for time series classification: a review" published in Data Mining and Knowledge Discovery, also available on ArXiv. It is built using Python, Deep Learning. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
This is the companion repository for our paper titled "Deep learning for time series classification: a review" published in Data Mining and Knowledge Discovery, also available on ArXiv.
Assuming you have docker installed. You can now use the docker image provided here.
Now that you have the data and the code you can just run the code.
2. Objective
Deep Learning for Time Series Classification
This project demonstrates how Python, Deep Learning can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later
- pip / virtualenv for dependencies
- VS Code, PyCharm or Jupyter Notebook
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/hfawaz/dl-4-tsc.git
cd dl-4-tscFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by hfawaz and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.
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